AMPO: Active Multi-Preference Optimization for Self-play Preference Selection
This addresses the problem of efficient and robust alignment in large language models for AI developers, though it is incremental as it builds on existing multi-preference optimization methods.
The paper tackles the computational infeasibility of using all candidate responses in multi-preference optimization for language-model alignment by proposing AMPO, which actively selects a small, informative subset for training, achieving state-of-the-art results on AlpacaEval with models like Llama 8B and Mistral 7B.
Multi-preference optimization enriches language-model alignment beyond pairwise preferences by contrasting entire sets of helpful and undesired responses, thereby enabling richer training signals for large language models. During self-play alignment, these models often produce numerous candidate answers per query, rendering it computationally infeasible to include all responses in the training objective. In this work, we propose $\textit{Active Multi-Preference Optimization}$ (AMPO), a novel approach that combines on-policy generation, a multi-preference group-contrastive loss, and active subset selection. Specifically, we score and embed large candidate pools of responses and then select a small, yet informative, subset that covers reward extremes and distinct semantic clusters for preference optimization. Our contrastive training scheme is capable of identifying not only the best and worst answers but also subtle, underexplored modes that are crucial for robust alignment. Theoretically, we provide guarantees for expected reward maximization using our active selection method, and empirically, AMPO achieves state-of-the-art results on $\textit{AlpacaEval}$ using Llama 8B and Mistral 7B. We release our datasets $\href{https://huggingface.co/Multi-preference-Optimization}{here}$.